class_docstr: Encode categorical integer features using a one-hot aka one-of-K scheme.
  
  The input to this transformer should be a sparse matrix of integers, denoting
  the values taken on by categorical (discrete) features. The output will be
  a sparse matrix were each column corresponds to one possible value of one
  feature. It is assumed that input features take on values in the range
  [0, n_values).
  
  Attributes
  ----------
  `feature_indices_` : array of shape (n_features,)
      Indices to feature ranges.
      Feature ``i`` in the original data is mapped to features
      from ``feature_indices_[i]`` to ``feature_indices_[i+1]``
      (and then potentially masked by `active_features_` afterwards)
  
  `n_values_` : array of shape (n_features,)
      Maximum number of values per feature.
docstr: Fit OneHotEncoder to X.
  
  Parameters
  ----------
  X : array-like, shape=(n_samples, n_feature)
      Input array of type int.
  
  Returns
  -------
  self
docstr_tok: ["Fit","OneHotEncoder","to","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape=","(","n_samples",",","n_feature",")","Input","array","of","type","int",".","Returns","--","--","--","-","self"]
code: def fit(self, X, y=None):
      """Fit OneHotEncoder to X.
  
      Parameters
      ----------
      X : array-like, shape=(n_samples, n_feature)
          Input array of type int.
  
      Returns
      -------
      self
      """
      self.fit_transform(X)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/implementations/SparseOneHotEncoder.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
label_desc: Use image node_id 1 for calling the SparseOneHotEncoder obj's underlying member method code with example usage: obj.fit(X, y) and returns: self